Hazeline Asuncion, Ph.D.
Dr. Asuncion received her Ph.D. in computer science from the University of California, Irvine, in 2009. Prior to coming to UW Bothell, she was a Postdoctoral Researcher in the Institute for Software Research at the University of California, Irvine. She has also worked in industry in a variety of roles: as a software engineer at Unisys Corporation and as a traceability engineer at Wonderware Corporation where she designed a successful in-house traceability system.
Her research emphasis is on traceability and she has developed a novel software traceability approach that automatically links distributed and heterogeneous information. She has investigated the tracing of software license conflicts in heterogeneously composed software systems. Dr. Asuncion is also interested in investigating the traceability challenges in other domains such as e-Science and health care.
Dr. Asuncion research focus is on traceability, i.e. the identification of related information that may be scattered across different files, different locations, and may be owned by different groups of people. Managing related information is a fundamental task in many contexts. In software engineering, relating the design to requirements is necessary in ensuring that the system to be developed meets customer requirements. In e-Science, identifying the relationships between intermediate data sets is necessary in the repeatability of experiments.
In software engineering, she is currently developing lightweight automated support for capturing design decisions based on analyzing changes to software documents (FACTS project). Possible projects:
Automatically extract changes to documents (e.g., MS Word) over time and visualizing these changes.
Applying machine learning techniques, such as topic modeling, on source code and other artifacts.
Connecting the changes to artifacts to specific project decisions and visualizing these interconnections.
Another research project is concerned with tracing features across related products (product line architectures). This research project entails developing tool-specific adapters to monitor user interactions with the data across different tools.
Data provenance research is concerned with automatically capturing the history behind the data within the context of the scientists work environment. Possible projects:
Monitoring events in different operating systems (e.g., UNIX, Win).
Capture links to experiment metadata by building tool-specific adapters for scientific tools (e.g. MatLab).
Analyzing the captured provenance with reasoners, IR techniques, machine learning techniques such as topic modeling.
Developing a provenance linkbase and a visualization of provenance links.
University of California, Irvine